{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4ad9a455-e16f-4af1-a6ed-1b83fabb1aa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['KaiTi']\n",
    "plt.rcParams['font.serif'] = ['KaiTi']\n",
    "df1 = pd.DataFrame({'城市':['北京','上海','广州','深圳','杭州','厦门'],'房价': [62428,53950,31375,57259,30447,37539]})\n",
    "df2 = pd.DataFrame({'城市':['北京','上海','广州','深圳','杭州','厦门','南京'],'人口密度（人/每平方公里）': [1267,3814,1760,6889,8268,2078,1103]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "49ae06b0-bb27-4f84-9f03-14222c415ef2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>房价</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京</td>\n",
       "      <td>62428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>53950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州</td>\n",
       "      <td>31375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>57259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>杭州</td>\n",
       "      <td>30447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>厦门</td>\n",
       "      <td>37539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   城市     房价\n",
       "0  北京  62428\n",
       "1  上海  53950\n",
       "2  广州  31375\n",
       "3  深圳  57259\n",
       "4  杭州  30447\n",
       "5  厦门  37539"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 看看内容\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "47717686-5e74-45f0-8d67-dfdf6e17d0b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>人口密度（人/每平方公里）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>3814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州</td>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>杭州</td>\n",
       "      <td>8268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>厦门</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>南京</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   城市  人口密度（人/每平方公里）\n",
       "0  北京           1267\n",
       "1  上海           3814\n",
       "2  广州           1760\n",
       "3  深圳           6889\n",
       "4  杭州           8268\n",
       "5  厦门           2078\n",
       "6  南京           1103"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e306183a-3431-4b58-8fe8-3961ad3e0796",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>房价</th>\n",
       "      <th>人口密度（人/每平方公里）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京</td>\n",
       "      <td>62428</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>53950</td>\n",
       "      <td>3814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州</td>\n",
       "      <td>31375</td>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>57259</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>杭州</td>\n",
       "      <td>30447</td>\n",
       "      <td>8268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>厦门</td>\n",
       "      <td>37539</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   城市     房价  人口密度（人/每平方公里）\n",
       "0  北京  62428           1267\n",
       "1  上海  53950           3814\n",
       "2  广州  31375           1760\n",
       "3  深圳  57259           6889\n",
       "4  杭州  30447           8268\n",
       "5  厦门  37539           2078"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.merge(df1,df2,on='城市')\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6f55efbd-d2b7-4fed-936e-c20a3e9bf597",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>房价</th>\n",
       "      <th>人口密度（人/每平方公里）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京</td>\n",
       "      <td>62428</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>53950</td>\n",
       "      <td>3814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州</td>\n",
       "      <td>31375</td>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>57259</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>杭州</td>\n",
       "      <td>30447</td>\n",
       "      <td>8268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>厦门</td>\n",
       "      <td>37539</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   城市     房价  人口密度（人/每平方公里）\n",
       "0  北京  62428           1267\n",
       "1  上海  53950           3814\n",
       "2  广州  31375           1760\n",
       "3  深圳  57259           6889\n",
       "4  杭州  30447           8268\n",
       "5  厦门  37539           2078"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.merge(df1,df2,how='left',on='城市')\n",
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "b213d1e7-3167-478c-8860-43667c790dbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>房价</th>\n",
       "      <th>人口密度（人/每平方公里）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京</td>\n",
       "      <td>62428.0</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>53950.0</td>\n",
       "      <td>3814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州</td>\n",
       "      <td>31375.0</td>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>57259.0</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>杭州</td>\n",
       "      <td>30447.0</td>\n",
       "      <td>8268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>厦门</td>\n",
       "      <td>37539.0</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>南京</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   城市       房价  人口密度（人/每平方公里）\n",
       "0  北京  62428.0           1267\n",
       "1  上海  53950.0           3814\n",
       "2  广州  31375.0           1760\n",
       "3  深圳  57259.0           6889\n",
       "4  杭州  30447.0           8268\n",
       "5  厦门  37539.0           2078\n",
       "6  南京      NaN           1103"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df5 = pd.merge(df1,df2,how='right',on='城市')\n",
    "df5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "9945cb1a-f575-45da-b178-df863b051766",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>城市</th>\n",
       "      <th>房价</th>\n",
       "      <th>人口密度（人/每平方公里）</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>北京</td>\n",
       "      <td>62428</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>上海</td>\n",
       "      <td>53950</td>\n",
       "      <td>3814</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>广州</td>\n",
       "      <td>31375</td>\n",
       "      <td>1760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳</td>\n",
       "      <td>57259</td>\n",
       "      <td>6889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>杭州</td>\n",
       "      <td>30447</td>\n",
       "      <td>8268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>厦门</td>\n",
       "      <td>37539</td>\n",
       "      <td>2078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>西安</td>\n",
       "      <td>14211</td>\n",
       "      <td>1205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>太原</td>\n",
       "      <td>11748</td>\n",
       "      <td>759</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   城市     房价  人口密度（人/每平方公里）\n",
       "0  北京  62428           1267\n",
       "1  上海  53950           3814\n",
       "2  广州  31375           1760\n",
       "3  深圳  57259           6889\n",
       "4  杭州  30447           8268\n",
       "5  厦门  37539           2078\n",
       "0  西安  14211           1205\n",
       "1  太原  11748            759"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df8 = pd.DataFrame({'城市':['西安','太原'],'房价': [14211,11748],'人口密度（人/每平方公里）': [1205,759]})\n",
    "df9 = pd.concat([df4,df8])\n",
    "df9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "150d25b4-a297-47bf-92ec-12b990fae651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='城市'>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df9.set_index('城市',inplace=True)\n",
    "df9.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b36271e7-e6a1-4481-b6f6-00df391c5074",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
